A Novel Population Initialization Method via Adaptive Experience Transfer for General-Purpose Binary Evolutionary Optimization

2025-11-29

AI Summary

In binary evolutionary optimization, the quality of the initial population critically affects algorithm performance under low-budget conditions (i.e., limited function evaluations), yet existing initialization methods often rely on problem-specific prior knowledge or fail to generalize across diverse real-world problems. Method: This paper proposes an adaptive, experience-based transfer initialization method that requires no problem-specific prior knowledge. It introduces a generalizable framework for representing, selecting, and transferring solution patterns—dynamically accumulating high-quality empirical knowledge from canonical benchmark problems into an experience repository—and employs a hybrid transfer strategy to adapt these patterns to unseen, complex, real-world problems and high-dimensional instances. Contribution/Results: Seamlessly integrated with standard evolutionary algorithms, the method demonstrates consistent effectiveness across six benchmark problem classes. Notably, it significantly outperforms state-of-the-art generic initialization approaches on three previously unseen real-world problems—validating its strong cross-problem generalization capability and computational efficiency under stringent evaluation budgets.

Technology Category

• Search and Optimization: Evolutionary Computation
• Machine Learning: Evolutionary Learning
• Constraint Satisfaction and Optimization: Constraint Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience

Abstract

Evolutionary Algorithms (EAs) are widely used general-purpose optimization methods due to their domain independence. However, under a limited number of function evaluations (#FEs), the performance of EAs is quite sensitive to the quality of the initial population. Obtaining a high-quality initial population without problem-specific knowledge remains a significant challenge. To address this, this work proposes a general-purpose population initialization method, named mixture-of-experience for population initialization (MPI), for binary optimization problems where decision variables take values of 0 or 1. MPI leverages solving experiences from previously solved problems to generate high-quality initial populations for new problems using only a small number of FEs. Its main novelty lies in a general-purpose approach for representing, selecting, and transferring solving experiences without requiring problem-specific knowledge. Extensive experiments are conducted across six binary optimization problem classes, comprising three classic classes and three complex classes from real-world applications. The experience repository is constructed solely based on instances from the three classic classes, while the performance evaluation is performed across all six classes. The results demonstrate that MPI effectively transfers solving experiences to unseen problem classes (i.e., the complex ones) and higher-dimensional problem instances, significantly outperforming existing general-purpose population initialization methods.

Problem

• Develops a general-purpose initialization method for binary evolutionary optimization
• Transfers solving experiences from previous problems to new ones without domain knowledge
• Enhances performance on unseen and higher-dimensional binary optimization problems

Innovation

• Adaptive experience transfer for population initialization
• General-purpose representation and transfer of solving experiences
• Mixture-of-experience method enhances binary evolutionary optimization